require(testthat)
require(EdSurvey)
options(width = 500)
options(useFancyQuotes = FALSE)
options(digits = 7)

sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))
source("REF-0-main.R") # has REF output in it
# ideally this wouldn't trip up any of the scope fixes below
dsex <- "should not be used"

context("wd is set correctly") # When this fails all regression tests are invalid.
test_that("wd is set correctly", {
  skip_on_cran()
  expect_true(file.exists("test-10-main.R"))
})

context("Primer reads in correctly")
test_that("Primer reads in correctly", {
  expect_is(sdf, "edsurvey.data.frame")
  expect_equal(dim(sdf), c(17606, 303))
  expect_equal(c(nrow(sdf), ncol(sdf)), c(17606, 303))
})

context("[, [[, [<-, [[<- edsurvey.data.frame")
test_that("[, [[, [<-, [[<- edsurvey.data.frame", {
  sdf2 <- sdf
  testVal <- sdf[ , "iep"]
  #test index number
  expect_equal(sdf2[ , 6], testVal)
  expect_equal(sdf2[[6]], testVal)
  
  #when assigned here, the index shifts since it will be saved to the esdf 'cache' data.frame and that is first listed in 'colnames'
  #we should document or note this somewhere
  sdf2[ , 6] <- testVal
  expect_equal(sdf2[ , 2], sdf[[6]], check.attributes = FALSE) #factor re-assignment won't exactly match attributes
  
  #note the origial index was 6, it was changed to 2 from first re-assignment
  sdf2[[2]] <- testVal
  expect_equal(sdf2[[2]], sdf[ , 6], check.attributes = FALSE) #factor re-assignment won't exactly match attributes
  
  #test by column name
  expect_equal(sdf2[ , "iep"], testVal, check.attributes = FALSE)
  expect_equal(sdf2[["iep"]], testVal, check.attributes = FALSE)
  
  testVal <- sdf[["dsex"]]
  sdf2[ , "dsex"] <- testVal
  expect_equal(sdf2[ , "dsex"], sdf[["dsex"]], check.attributes = FALSE)
  
  sdf2[["dsex"]] <- testVal
  expect_equal(sdf2[ , "dsex"], sdf[["dsex"]], check.attributes = FALSE)
  
  expect_error(sdf2[ , 999999], "Column index out of range.*")
  expect_error(sdf2[ , "NOT A COLUMN ZZZZ"], "The following variable names are required for this call.*")
  
  expect_error(sdf2[[9999999]] <- testVal, "Column index out of range.*")
  expect_error(sdf2[["NOT A COLUMN ZZZ"]] <- testVal, "Cannot find.*as a column name or list item in this object")
})

context("$ assign")
test_that("$ assign", {
  # subset then assign
  sdfM <- subset(sdf, dsex == "Male")
  sdfM$books <- ifelse(sdfM$b013801 %in% c("0-10", "11-25"), "0-25 books", "26+ books")
  sdf$books <- ifelse(sdf$b013801 %in% c("0-10", "11-25"), "0-25 books", "26+ books")
  sdfM2 <- subset(sdf, dsex == "Male")
  tab <- table(sdfM$books, sdfM2$books)
  diag(tab) <- 0
  expect_equal(as.vector(tab), rep(0, 4))
  # assign a new variable
  sdf$a <- sdf$dsex
  tab <- table(sdf$a, sdf$dsex)
  expect_equal(tab, assignTableREF)
  # overwrite a variable, common for recoding
  # note: dsex has no NAs on the reporting sample
  sdf$dsex <- ifelse(sdf$dsex %in% "Male", "boy", "girl")
  tab2 <- table(sdf$a, sdf$dsex)
  expect_equal(unname(tab2), unname(assignTableREF))

  expect_warning(sdf$a[1:5] <- "invalid", "factor level")
  # repeated to be sure this does not throw an error, which it used to
  expect_warning(sdf$a[1:5] <- "invalid", "factor level")
  # reset sdf
  sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))
})

context("showPlausibleValues and showWeights verbose output agrees")
test_that("showPlausibleValues and showWeights verbose output agrees", {
  skip_on_cran()
  spv <- c(
    "There are 6 subject scale(s) or subscale(s) in this edsurvey.data.frame:",
    "'num_oper' subject scale or subscale with 5 plausible values.",
    "  The plausible value variables are: 'mrps11', 'mrps12', 'mrps13', 'mrps14', and 'mrps15'",
    "", "'measurement' subject scale or subscale with 5 plausible values.",
    "  The plausible value variables are: 'mrps21', 'mrps22', 'mrps23', 'mrps24', and 'mrps25'",
    "", "'geometry' subject scale or subscale with 5 plausible values.",
    "  The plausible value variables are: 'mrps31', 'mrps32', 'mrps33', 'mrps34', and 'mrps35'",
    "", "'data_anal_prob' subject scale or subscale with 5 plausible values.",
    "  The plausible value variables are: 'mrps41', 'mrps42', 'mrps43', 'mrps44', and 'mrps45'",
    "", "'algebra' subject scale or subscale with 5 plausible values.",
    "  The plausible value variables are: 'mrps51', 'mrps52', 'mrps53', 'mrps54', and 'mrps55'",
    "", "'composite' subject scale or subscale with 5 plausible values (the default).",
    "  The plausible value variables are: 'mrpcm1', 'mrpcm2', 'mrpcm3', 'mrpcm4', and 'mrpcm5'",
    ""
  )
  co <- capture.output(showPlausibleValues(sdf, verbose = TRUE))
  expect_equal(co, spv)
  skip_on_cran()
  sw <- c(
    "There is 1 full sample weight in this edsurvey.data.frame:",
    "  'origwt' with 62 JK replicate weights (the default).", "    Jackknife replicate weight variables associated with the full sample weight 'origwt':",
    "    'srwt01', 'srwt02', 'srwt03', 'srwt04', 'srwt05', 'srwt06', 'srwt07', 'srwt08', 'srwt09', 'srwt10', 'srwt11', 'srwt12', 'srwt13', 'srwt14', 'srwt15', 'srwt16', 'srwt17', 'srwt18', 'srwt19', 'srwt20', 'srwt21', 'srwt22', 'srwt23', 'srwt24', 'srwt25', 'srwt26', 'srwt27', 'srwt28', 'srwt29', 'srwt30', 'srwt31', 'srwt32', 'srwt33', 'srwt34', 'srwt35', 'srwt36', 'srwt37', 'srwt38', 'srwt39', 'srwt40', 'srwt41', 'srwt42', 'srwt43', 'srwt44',",
    "    'srwt45', 'srwt46', 'srwt47', 'srwt48', 'srwt49', 'srwt50', 'srwt51', 'srwt52', 'srwt53', 'srwt54', 'srwt55', 'srwt56', 'srwt57', 'srwt58', 'srwt59', 'srwt60', 'srwt61', and 'srwt62'",
    ""
  )
  withr::with_options(
    list(digits = 4, width = 500),
    co <- capture.output(showWeights(sdf, verbose = TRUE))
  )
  expect_equal(co, sw)
})

context("searchSDF")
test_that("searchSDF", {
  search1 <- searchSDF(string = c("home", "book"), data = sdf)
  search2 <- searchSDF(string = c("home|book"), data = sdf)
  search3 <- searchSDF(string = "value", data = sdf, levels = TRUE)

  # drop 'fileFormat' var for quick comparison, update the RDS when able
  search1$fileFormat <- NULL
  search2$fileFormat <- NULL
  search3$fileFormat <- NULL

  search2_ref <- structure(list(variableName = c("b013801", "b017001", "b017101", 
"b018201", "b017451", "t088804", "t088805", "t091503"), Labels = c("Books in home", 
"Newspaper in home", "Computer at home", "Language other than English spoken in home", 
"Talk about studies at home", "Computer activities: Use a gradebook program", 
"Computer activities: Post homework,schedule info", "G8Math:How often use Geometry sketchbook"
)), row.names = c(NA, 8L), class = "data.frame")
  search3_ref <- structure(list(variableName = c("m086101", "m020001", "m143601", 
"m142301"), Labels = c("Read value from graph", "Apply place value                            (R1)", 
"Solve for x given value of n", "Identify place value"), Levels = c("1. A; 2. B; 3. C *; 4. D; 5. E; 8. Omitted; 9. Not Reached; 0. Multiple", 
"1. Incorrect; 2. Correct*; 5. Illegible; 6. Off Task; 7. Non-Rateable; 8. Omitted; 9. Not Reached", 
"1. A; 2. B; 3. C; 4. D *; 5. E; 8. Omitted; 9. Not Reached; 0. Multiple", 
"1. A; 2. B; 3. C; 4. D; 5. E *; 8. Omitted; 9. Not Reached; 0. Multiple"
)), row.names = c("Student.131", "Student.165", "Student.177", "Student.214"), class = c("searchSDF", "data.frame"))
  expect_equal(search1, structure(list(variableName = "b013801", Labels = "Books in home"), row.names = 1L, class = "data.frame"))
  expect_equal(search2, search2_ref)
  expect_equal(search3, search3_ref)
})

context("showCodebook")
test_that("showCodebook", {
  cb <- showCodebook(sdf, "school")
  sdfRecode <- recode.sdf(sdf, recode = list(dsex = list(from = c("Male"), to = c("MALE"))))
  cb2 <- showCodebook(sdfRecode, c("student", "school"), labelLevels = FALSE, includeRecodes = TRUE)

  expect_known_value(cb, file = "showCodebook.rds", update = FALSE)
  expect_known_value(cb2, file = "showCodebookRecodes.rds", update = FALSE)
})

context("getData")
test_that("getData", {
  expect_known_value(head(gd1 <- getData(sdf, c("dsex", "b017451"))), file = "gd1.rds", update = FALSE)
  skip_on_cran()
  expect_known_value(head(gd2 <- getData(sdf, c("dsex", "b017451"), defaultConditions = FALSE)), file = "gd2.rds", update = FALSE)
  expect_known_value(head(gd3 <- getData(sdf, c("dsex", "b017451"), dropUnusedLevels = FALSE)), file = "gd3.rds", update = FALSE)
  expect_known_value(head(gd4 <- getData(sdf, c("dsex", "b017451"), dropOmittedLevels = TRUE, includeNaLabel = FALSE)), file = "gd4.rds", update = FALSE)
  expect_known_value(head(gd5 <- getData(sdf, c("dsex", "b017451"), dropOmittedLevels = TRUE, includeNaLabel = TRUE)), file = "gd5.rds", update = FALSE)
  gd6 <- getData(sdf, formula = composite ~ dsex + b017451)
  gd6 <- gd6[c(1:50, (nrow(gd6) - 50):nrow(gd6)), ] # this file was larger. slim down a bit.
  expect_known_value(gd6, file = "gd6.rds", update = FALSE, check.attributes = FALSE) # TEMPORARILY HAVE IT SKIP ATTRIBUTE CHECKS
  gddat <- getData(sdf,
    c("composite", "geometry", "dsex", "sdracem", "pared", "b017451", "origwt"),
    addAttributes = TRUE, dropOmittedLevels = FALSE
  )
  gddat <- gddat[c(1:50, (nrow(gddat) - 50):nrow(gddat)), ] # this file was larger. slim down a bit.
  attributes(gddat)$dataList$Student$lafObject <- NULL
  attributes(gddat)$dataList$School$lafObject <- NULL
  attributes(gddat)$fr2Path <- NULL
  attributes(gddat)$scoreCard <- NULL
  attributes(gddat)$dichotParamTab <- NULL
  attributes(gddat)$polyParamTab <- NULL
  attributes(gddat)$adjustedData <- NULL
  attributes(gddat)$scoreFunction <- NULL
  attributes(gddat)$testData <- NULL
  attributes(gddat)$scoreDict <- NULL
  attributes(gddat)$cacheDataLevelName <- NULL
  attributes(gddat)$dataList$Student$conflictLevels <- NULL
  attributes(gddat)$dataList$School$conflictLevels <- NULL
  expect_known_value(gddat, file = "gddat.rds", update = FALSE)

  expect_known_value(head(gd7 <- getData(sdf, c("dsex", "b017451"))), file = "gd7.rds", update = FALSE)
  expect_known_value(head(gd8 <- getData(sdf, c("dsex", "c052601"), dropUnusedLevels = FALSE)), file = "gd8.rds", update = FALSE) # schoolMergeVarStudent="scrpsu", schoolMergeVarSchool="sscrpsu"
  df2 <- getData(sdf, c("dsex", "b017451"),
    recode = list(
      b017451 = list(
        from = c(
          "Never or hardly ever",
          "Once every few weeks",
          "About once a week"
        ),
        to = c("Infrequently")
      ),
      b017451 = list(
        from = c(
          "2 or 3 times a week",
          "Every day"
        ),
        to = c("Frequently")
      )
    )
  )
  expect_known_value(head(df2), file = "df2.rds", update = FALSE)
  # use recode for both recodes
  df2B <- recode.sdf(sdf, recode = list(
    b017451 = list(
      from = c(
        "Never or hardly ever",
        "Once every few weeks",
        "About once a week"
      ),
      to = c("Infrequently")
    ),
    b017451 = list(
      from = c(
        "2 or 3 times a week",
        "Every day"
      ),
      to = c("Frequently")
    )
  ))
  df2C <- getData(df2B, c("dsex", "b017451"))
  expect_equal(df2, df2C)
  # use recode for just one and get data for the other
  df2D <- recode.sdf(sdf, recode = list(b017451 = list(
    from = c(
      "Never or hardly ever",
      "Once every few weeks",
      "About once a week"
    ),
    to = c("Infrequently")
  )))
  df2E <- getData(df2D, c("dsex", "b017451"),
    recode = list(b017451 = list(
      from = c(
        "2 or 3 times a week",
        "Every day"
      ),
      to = c("Frequently")
    ))
  )
  expect_equal(df2, df2E)
  # numeric recodes
  df3 <- getData(sdf,
    c("dsex", "b017451"),
    recode = list(
      b017451 = list(
        from = c(1, 2, 3),
        to = c("Infrequently")
      ),
      b017451 = list(
        from = c(4, 5),
        to = c("Frequently")
      )
    )
  )
  expect_equal(df2, df3) # recode by label and numeric agree

  sdf_males <- EdSurvey:::subset(sdf, dsex == "Male", verbose = FALSE)
  expect_equal(dim(sdf_males), c(8905, 303))

  sdf_males <- EdSurvey:::subset(sdf, dsex %in% "Male", verbose = FALSE)
  expect_equal(dim(sdf_males), c(8905, 303))

  # test bad subset, a$bb does not exist
  expect_error(EdSurvey:::subset(sdf, dsex %in% a$bb, verbose = FALSE))
  expect_error(EdSurvey:::subset(sdf, dsex %in% bb$a, verbose = FALSE))

  # test subset using an element from the parent frame
  # does not work in testthat with test()
  if (FALSE) {
    print(search())
    env <- new.env(hash = TRUE, parent = .GlobalEnv, size = 1L)
    assign("a", list(b = "Male"), envir = env)
    sdf_males <- with(env, EdSurvey:::subset(sdf, dsex %in% a$b, verbose = FALSE))
    expect_equal(dim(sdf_males), c(8905, 303))
  }
})

context("getData order of userConditions")
test_that("getData order of userConditions", {
  # subset first, then recode
  sdf_males <- subset(sdf, dsex %in% "Male")
  sdf_males <- recode.sdf(sdf_males, recode = list(dsex = list(from = "Male", to = "Boy")))
  gdat1 <- getData(sdf_males, c("dsex"))
  expect_equal(nrow(gdat1), 8486)
  expect_equal(unique(as.character(gdat1$dsex)), "Boy")

  # recode first then subset
  sdf_males <- recode.sdf(sdf, recode = list(dsex = list(from = "Male", to = "Boy")))
  sdf_males <- subset(sdf_males, dsex %in% "Boy")
  gdat2 <- getData(sdf_males, c("dsex"))
  expect_equal(gdat2, gdat1)
})

context("rename.sdf")
test_that("rename.sdf", {
  skip_on_cran()
  # check rename only
  sdf_rename <- rename.sdf(sdf, c("dsex", "composite", "origwt"), c("gender", "composite0", "totwgt"))
  expect_equal(attr(getAttributes(sdf_rename, "pvvars"), "default"), "composite0")
  expect_equal(attr(getAttributes(sdf_rename, "weights"), "default"), "totwgt")
  gDat1 <- getData(sdf, c("dsex", "composite", "origwt"))
  gDat2 <- getData(sdf_rename, c("gender", "composite0", "totwgt"))
  names(gDat1) <- NULL
  names(gDat2) <- NULL
  expect_equal(gDat1, gDat2)
  expect_equal(levelsSDF("dsex", sdf)$dsex, levelsSDF("gender", sdf_rename)$gender)

  # check rename along with subset and recode.sdf
  sdf_males <- subset(sdf, dsex %in% "Male")
  gDat1 <- getData(sdf_males, "dsex")
  sdf_males <- rename.sdf(sdf_males, "dsex", "gender")
  sdf_males <- recode.sdf(sdf_males, list(gender = list(from = "Male", to = "Boy")))
  gDat2 <- getData(sdf_males, "gender")
  expect_equal(as.numeric(table(gDat1$dsex)), as.numeric(table(gDat2$gender)))

  # complicated order of operations
  sdf_rename <- rename.sdf(sdf, "sdracem", "race")
  sdf_rename <- subset(sdf_rename, !race %in% c("Omitted"))
  sdf_rename <- rename.sdf(sdf_rename, "race", "race_recode")
  sdf_rename <- recode.sdf(sdf_rename, recode = list(race_recode = list(
    from = c("Hispanic", "Asian/Pacific Island", "Amer Ind/Alaska Natv"),
    to = "Other"
  )))
  sdf_rename <- subset(sdf_rename, race_recode %in% "Other")
  expect_equal(nrow(sdf_rename), nrow(subset(sdf, sdracem %in% c("Hispanic", "Asian/Pacific Island", "Amer Ind/Alaska Natv", "Other"))))
})

context("subset throws an error")
test_that("subset throws an error", {
  expect_error(sdf_error <- subset(sdf, dsex1 %in% "Male"))
  expect_error(sdf_error <- subset(sdf, dsex == "Male" & dsex1 == "Male"))
})

context("lm.sdf")
lm1 <- lm.sdf(~ dsex + b017451, sdf)
lm1$data <- NULL
lm1$call <- NULL

test_that("lm.sdf", {
  skip_on_cran()
  expect_equal(lapply(lm1, head), lm1_head)

  lm1S <- lm.sdf(~ dsex + b017451, sdf, standardizeWithSamplingVar = TRUE)
  withr::with_options(
    list(digits = 4),
    slm1Scoef <- capture.output(summary(lm1S, src = TRUE)$coefmat)
  )
  expect_equal(slm1Scoef, stdCoefREF)

  lm10 <- lm.sdf(composite ~ dsex + b017451, sdf)
  lm10B <- lm.sdf(composite ~ dsex + b017451, sdf, weightVar = "origwt")
  lm10C <- lm.sdf(composite ~ dsex + b017451, sdf, weightVar = origwt)
  lm10D <- lm.sdf(composite ~ dsex + b017451, sdf, weightVar = c("origwt"))
  expect_equal(coef(lm10), coef(lm10B))
  expect_equal(coef(lm10), coef(lm10C))
  expect_equal(coef(lm10), coef(lm10D))

  lm10 <- lm.sdf(composite ~ dsex + b017451, sdf)
  lm10$data <- NULL
  lm10$call <- NULL
  expect_equal(lapply(lm10, head), lm10_head)
  lm1f <- lm.sdf(composite ~ dsex + b017451, sdf, relevels = list(dsex = "Female"))
  lm1f$data <- NULL
  lm1f$residuals <- head(lm1f$residuals)
  lm1f$PV.residuals <- head(lm1f$PV.residuals)
  lm1f$PV.fitted.values <- head(lm1f$PV.fitted.values)
  expect_known_value(lapply(lm1f, head), "lm1f.rds", update = FALSE)
  lm1re <- lm.sdf(composite ~ dsex + b017451, sdf, recode = list(dsex = list(from = "Male", to = "MALE")))
  lm1re$data <- NULL
  lm1re$residuals <- head(lm1re$residuals)
  lm1re$PV.residuals <- head(lm1re$PV.residuals)
  lm1re$PV.fitted.values <- head(lm1re$PV.fitted.values)
  expect_known_value(lapply(lm1re, head), "lm1re.rds", update = FALSE)
  # test that lfactor levels can be used in relevels argument
  lm1f2 <- lm.sdf(composite ~ dsex + b017451, sdf, jrrIMax = 1, relevels = list(dsex = 2))
  lm1f2$data <- NULL
  lm1f2$residuals <- head(lm1f2$residuals)
  lm1f2$PV.residuals <- head(lm1f2$PV.residuals)
  lm1f2$PV.fitted.values <- head(lm1f2$PV.fitted.values)
  # calls will not be equal
  lm1f$call <- lm1f2$call <- NULL
  expect_equal(lm1f, lm1f2)
  expect_equal(summary(lm1f), summary(lm1f2))
})

context("lm.sdf Taylor series")

expect_is(
  sdf_taylor <- lm.sdf(composite ~ sdracem + dsex + pared,
    subset(sdf, pared == 1 | pared == 2, verbose = FALSE),
    weightVar = "origwt",
    varMethod = "Taylor",
    jrrIMax = Inf
  ),
  "edsurveyLm"
)
expect_is(
  sdf_taylor <- lm.sdf(composite ~ sdracem + dsex + pared,
    data = subset(sdf, pared == 1 | pared == 2, verbose = FALSE),
    weightVar = "origwt",
    varMethod = "Taylor",
    jrrIMax = Inf
  ),
  "edsurveyLm"
)
sdf_taylorWV <- lm.sdf(composite ~ sdracem + dsex + pared,
  subset(sdf, pared == 1 | pared == 2, verbose = FALSE),
  weightVar = origwt,
  varMethod = "Taylor",
  jrrIMax = Inf
)
sdf_taylorWV$call <- sdf_taylor$call <- NULL
expect_equal(sdf_taylor, sdf_taylorWV)
lm1t <- lm.sdf(composite ~ dsex + b017451, sdf, varMethod = "Taylor")
lm1t$data <- NULL
lm1t$residuals <- head(lm1t$residuals)

lm1t$PV.residuals <- head(lm1t$PV.residuals)
lm1t$PV.fitted.values <- head(lm1t$PV.fitted.values)
expect_known_value(lapply(lm1t, head), "lm1t.rds", update = FALSE)
lm1jk <- lm.sdf(composite ~ dsex + b017451, sdf, varMethod = "jackknife")
expect_equal(coef(lm1t), coef(lm1jk))
lm1jk$data <- NULL
lm1jk$residuals <- head(lm1jk$residuals)
lm1jk$PV.residuals <- head(lm1jk$PV.residuals)
lm1jk$PV.fitted.values <- head(lm1jk$PV.fitted.values)
lm1jk <- summary(lm1jk, src = TRUE)
lm1jk.ref <- lm1
lm1jk.ref$formula <- NULL
lm1jk$call <- NULL
lm1jk$formula <- NULL
lm1jk$coefmat$stdCoef <- NULL
lm1jk$coefmat$stdSE <- NULL
expect_equal(lapply(lm1jk, head), lapply(lm1jk.ref, head))
# estimates should agree too
test_that("lm.sdf Taylor series", {
  skip_on_cran()

  lm2ta <- lm.sdf(composite ~ dsex + sdracem + yrsmath, sdf, varMethod = "Taylor")
  lm2jka <- lm.sdf(composite ~ dsex + sdracem + yrsmath, sdf, varMethod = "jackknife")
  # check only estimates
  expect_equal(coef(lm2ta), coef(lm2jka))

  lm2t <- lm.sdf(composite ~ dsex + sdracem + yrsmath, sdf, relevels = list(dsex = "Female"), varMethod = "Taylor")
  lm2t$data <- NULL
  lm2t$residuals <- head(lm2t$residuals)
  lm2t$PV.residuals <- head(lm2t$PV.residuals)
  lm2t$PV.fitted.values <- head(lm2t$PV.fitted.values)
  expect_known_value(lm2t, "lm2t.rds", update = FALSE)
  lm2jk <- lm.sdf(composite ~ dsex + sdracem + yrsmath, sdf, varMethod = "jackknife", relevel = list(dsex = "Female"), jrrIMax = 1)
  lm2jk$data <- NULL
  lm2jk$residuals <- head(lm2jk$residuals)
  lm2jk$PV.residuals <- head(lm2jk$PV.residuals)
  lm2jk$PV.fitted.values <- head(lm2jk$PV.fitted.values)
  lm2jk$call <- NULL
  expect_known_value(lm2jk, "lm2.rds", update = FALSE)
  expect_equal(coef(lm2t), coef(lm2jk))
})


context("edsurveyTable")
test_that("edsurveyTable", {
  skip_on_cran()
  # two levels, results checked vs Primer
  es1 <- edsurveyTable(composite ~ dsex + b017451, sdf, jrrIMax = 1)
  es1c <- capture.output(es1)
  expect_equal(es1c, es1REF)
  # test no LHS variable
  es10 <- edsurveyTable(~ dsex + b017451, sdf, jrrIMax = 1)
  es10c <- capture.output(es10)
  expect_equal(es10c, es1REF)

  # check for just males (dsex is only occupied at one level)
  sdfm <- subset(sdf, dsex == "Male", verbose = FALSE)
  es2 <- edsurveyTable(composite ~ dsex + b017451, sdfm, jrrIMax = Inf)
  es2c <- capture.output(es2)
  expect_equal(es2c, es2REF)
  # test dropOmittedLevels
  es2b <- edsurveyTable(composite ~ dsex + b017451, sdfm, jrrIMax = 1, dropOmittedLevels = FALSE)
  es2bc <- capture.output(es2b)
  expect_equal(es2bc, es2bREF)

  # test unbalanced tables, this check verified. See email from Ting on 10/1/2015 at 5:25 Eastern
  es3 <- edsurveyTable(composite ~ lep + ell3, sdf, jrrIMax = 1)
  es3c <- capture.output(es3)
  expect_equal(es3c, es3REF)
  # check return.means and return.sepct arguments
  es3b <- edsurveyTable(composite ~ lep + ell3, sdf, jrrIMax = 1, returnMeans = FALSE)
  expect_equal(es3b$data, es3b$data[, c("lep", "ell3", "N", "WTD_N", "PCT", "SE(PCT)")])
  es3c <- edsurveyTable(composite ~ lep + ell3, sdf, jrrIMax = 1, returnMeans = FALSE, returnSepct = FALSE)
  expect_equal(es3c$data, es3$data[, c("lep", "ell3", "N", "WTD_N", "PCT")])
  # test unbalanced tables, with three levels. This check verified. See email from Ting on 10/1/2015 at 5:25 Eastern
  es4 <- edsurveyTable(composite ~ lep + ell3 + dsex, sdf, jrrIMax = 1)
  es4c <- capture.output(es4)
  expect_equal(es4c, es4REF)
  # test LHS variable that isn't composite
  es11 <- edsurveyTable(b017451 ~ dsex, sdf)
  es11c <- capture.output(es11)
  expect_equal(es11c, es11REF1)
  # recode
  es1r <- edsurveyTable(composite ~ dsex + b017451, sdf, jrrIMax = 1, recode = list(dsex = list(from = "Male", to = "MALE")))
  es1rc <- capture.output(es1r)
  expect_equal(es1rc, es1rREF)
  # test omitted levels problematic sparseness case
  suppressWarnings(es5 <- edsurveyTable(composite ~ dsex + b017451 + b003501, data = sdf, dropOmittedLevels = FALSE, pctAggregationLevel = 2))
  es5c <- capture.output(es5)
  expect_equal(es5c, es5REF)
})

context("edsurveyTable with N=0")
test_that("edsurveyTable with N=0", {
  skip_on_cran()
  expect_warning(es0 <- edsurveyTable(~ b003501 + m815401, data = sdf, dropOmittedLevels = FALSE))
  esDF <- getData(sdf, c("b003501", "m815401"), dropOmittedLevels = FALSE)
  esDFtable <- as.data.frame(table(esDF))
  esDFtable <- esDFtable[order(esDFtable$b003501), ]
  expect_equal(nrow(es0$data), nrow(expand.grid(levels(esDF$m815401), levels(esDF$b003501))))
  expect_equal(es0$data$N, esDFtable$Freq)
  expect_equal(capture.output(es0), estwith0REF)

  expect_warning(es1 <- edsurveyTable(~ b003501 + m815401, data = sdf, dropOmittedLevels = FALSE, pctAggregationLevel = 0))
  expect_equal(nrow(es1$data), nrow(esDFtable))
})

context("edsurveyTable with no rhs variable")
test_that("edsurveyTable with no rhs variable", {
  skip_on_cran()
  es <- edsurveyTable(composite ~ 1, data = sdf, returnVarEstInputs = TRUE)
  esc <- capture.output(es)
  expect_equal(esc, es_norhsREF)
})


context("gap with N=0")
test_that("gap with N=0", {
  skip_on_cran()
  gap0 <- gap("b003501",
    data = sdf,
    groupA = m815401 %in% "Multiple",
    groupB = "default",
    dropOmittedLevels = FALSE, targetLevel = "Multiple",
    returnSimpleDoF = TRUE, returnVarEstInputs = TRUE,
    returnSimpleN = TRUE
  )
  gap0c <- capture.output(gap0)
  expect_equal(gap0c, gap0REF)
})

context("edsurveyTable2pdf")
test_that("edsurveyTable2pdf", {
  skip_on_cran()
  est1 <- edsurveyTable(composite ~ dsex + b017451, sdf)
  expect_equal(
    capture.output(edsurveyTable2pdf(
      data = est1,
      formula = b017451 ~ dsex,
      toCSV = "",
      filename = "CONSOLE",
      returnMeans = FALSE
    )),
    pdf_estREF
  )
})

context("edsurveyTable Taylor")
test_that("edsurveyTable Taylor", {
  skip_on_cran()
  es1t <- edsurveyTable(composite ~ dsex + b017451, sdf, jrrIMax = 1, varMethod = "Taylor")
  es1tc <- capture.output(es1t)
  # compare Taylor to jackknife, only columns that should agree
  es1j <- edsurveyTable(composite ~ dsex + b017451, sdf, jrrIMax = 1, varMethod = "jackknife")
  es1t$njk <- NULL
  es1j$njk <- NULL
  es1t$varMethod <- NULL
  es1j$varMethod <- NULL
  es1j$data["SE(PCT)"] <- NULL
  es1t$data["SE(PCT)"] <- NULL
  es1j$data["SE(MEAN)"] <- NULL
  es1t$data["SE(MEAN)"] <- NULL
  expect_equal(es1j, es1t)

  # check Taylor output
  expect_equal(es1tc, es1tREF)
  # test unbalanced tables, note some singleton PSUs
  expect_warning(es3t <- edsurveyTable(composite ~ lep + ell3, sdf, jrrIMax = Inf, varMethod = "Taylor"))
  es3tc <- capture.output(es3t)
  expect_equal(es3tc, es3tREF)
  # test unbalanced tables
  expect_warning(es4t <- edsurveyTable(composite ~ lep + ell3 + dsex, sdf, jrrIMax = 1, varMethod = "Taylor"))
  es4tc <- capture.output(es4t)
  expect_equal(es4tc, es4tREF)

  # check for just males (dsex is only occupied at one level)
  sdfm <- subset(sdf, dsex == "Male", verbose = FALSE)
  es2t <- edsurveyTable(composite ~ dsex + b017451, sdfm, jrrIMax = 1, varMethod = "Taylor")
  es2tc <- capture.output(es2t)
  expect_equal(es2tc, es2tREF)
})

test_that("variable label stored as attributes", {
  est1 <- edsurveyTable(composite ~ dsex + b017451, sdf, jrrIMax = 1)
  expect_equal(attr(est1$data$dsex, "label"), "Gender")
  expect_equal(attr(est1$data$b017451, "label"), "Talk about studies at home")
})

context("showCutPoints agrees")
test_that("showCutPoints agrees", {
  skip_on_cran()
  sw <- c(
    "Achievement Levels:",
    "  Mathematics:  262, 299, 333"
  )
  co <- capture.output(showCutPoints(sdf))
  expect_equal(sw, co)
})

context("updatePlausibleValue")
test_that("updatePlausibleValue", {
  skip_on_cran()
  lma <- lm.sdf(~dsex, sdf, varMethod = "Taylor")
  sdfb <- updatePlausibleValue("composite", "newname", sdf)
  lmb <- lm.sdf(~dsex, sdfb, varMethod = "Taylor")
  lmb$call <- lma$call <- NULL # the call is different
  lmb$formula <- lma$formula <- NULL # the formula has the default value substituted in and so is different
  lmb$data <- lma$data <- NULL
  expect_equal(lma, lmb)
})

context("percentile")
test_that("percentile", {
  skip_on_cran()
  expect_known_value(pct1 <- percentile("composite", c(0, 1, 25, 50, 75, 99, 100), sdf), "pct1.rds", update = FALSE)
  # percentiles should be -1* their value when multiplied by -1 and taken in reverse order
  tmpDat <- getData(data = sdf, varnames = c("composite", "origwt"), addAttributes = TRUE)
  pvs <- getPlausibleValue(data = sdf, "composite")
  for (pvi in pvs) {
    tmpDat[, pvi] <- -1 * tmpDat[, pvi]
  }
  pct1i <- percentile("composite", rev(c(0, 1, 25, 50, 75, 99, 100)), data = tmpDat, confInt = FALSE)
  expect_equal(pct1$estimate, -1 * pct1i$estimate)
  # range should agree with 0th and 100th percentile
  pct0 <- percentile("mrpcm1", c(0, 100), data = sdf)
  expect_equal(pct0$estimate, range(sdf$mrpcm1))
})

######################## GAP TESTS FAIL ################
context("return VarEstInputs")
lm1 <- lm.sdf(~ dsex + b017451, sdf, returnVarEstInputs = TRUE)
expect_known_value(lm1$varEstInputs, "lm_varest.rds", update = FALSE)
test_that("return VarEstInputs", {
  skip_on_cran()
  es1 <- edsurveyTable(composite ~ dsex + b017451, sdf, jrrIMax = 1, returnVarEstInputs = TRUE)
  expect_known_value(list(es1$meanVarEstInputs, es1$pctVarEstInputs), file = "est_varest.rds", update = FALSE)
  g1 <- gap("composite", sdf, dsex == "Male", returnVarEstInputs = TRUE)
  expect_known_value(list(g1$varEstInputs, g1$pctVarEstInputs), file = "gap1_varest.rds", update = FALSE)
  g2 <- gap("b017451", sdf, dsex == "Male", targetLevel = "Once every few weeks", returnVarEstInputs = TRUE)
  expect_known_value(list(g2$varEstInputs, g2$pctVarEstInputs), file = "gap2_varest.rds", update = FALSE)
  g3 <- gap("composite", sdf, dsex == "Male", returnVarEstInputs = TRUE, achievementLevel = c("At or aboVe Bas"))
  expect_known_value(list(g3$varEstInputs, g3$pctVarEstInputs), file = "gap3_varest.rds", update = FALSE)
  g3d <- gap("composite", sdf, dsex == "Male", returnVarEstInputs = TRUE, achievementLevel = c("At Basic"), achievementDiscrete = TRUE)
  expect_known_value(list(g3d$varEstInputs, g3d$pctVarEstInputs), file = "gap3d_varest.rds", update = FALSE)
  g4 <- gap("composite", sdf, dsex == "Male", percentiles = c(50), returnVarEstInputs = TRUE)
  expect_known_value(list(g4$varEstInputs, g4$pctVarEstInputs), file = "gap4_varest.rds", update = FALSE)
  pct1 <- percentile("composite", c(2, 50), sdf, returnVarEstInputs = TRUE)
  expect_known_value(attributes(pct1)$varEstInputs, file = "pct_varest.rds", update = FALSE)
  test1 <- achievementLevels(returnCumulative = TRUE, data = sdf, returnVarEstInputs = TRUE)
  expect_known_value(list(test1$discVarEstInputs, test1$cumVarEstInputs), file = "aLevels_varest.rds", update = FALSE)
  es1re <- edsurveyTable(composite ~ dsex + b017451, sdf, jrrIMax = 1, returnVarEstInputs = TRUE, recode = list(dsex = list(from = "Male", to = "MALE")))
  expect_known_value(list(es1re$meanVarEstInputs, es1re$pctVarEstInputs), file = "est_varest_recode.rds", update = FALSE)
  # add test per issue 671
  glab <- gap("composite", sdf, dsex == "Male", dsex == "Female", achievementLevel = c("below Basic", "At Bas"), achievementDiscrete = TRUE)
  expect_equal(glab$results$achievementLevel, c("below Basic", "At Basic"))
})

context("gap")
test_that("gap", {
  # gap SD
  expect_known_value(g0 <- gap("composite", sdf, dsex == "Male", dsex == "Female", returnSimpleDoF = TRUE, stDev = TRUE), "gap_main_SD.rds", update = FALSE)
  # gap means
  expect_known_value(g1 <- gap("composite", sdf, dsex == "Male", dsex == "Female", returnSimpleDoF = TRUE), "gap_main_mean.rds", update = FALSE)
  g1q <- gap("composite", sdf, "dsex==\"Male\"", "dsex==\"Female\"", returnSimpleDoF = TRUE)
  g1q$call <- g1$call # the call is different, so fix that
  expect_known_value(g1q, "gap_main_mean.rds", update = FALSE)
  skip_on_cran()
  # gap percentile
  expect_known_value(g2p <- gap("composite", sdf, dsex == "Male", dsex == "Female", percentile = c(0, 50, 98), returnSimpleDoF = TRUE), "gap_main_percentile.rds", update = FALSE)
  g2pq <- gap("composite", sdf, "dsex==\"Male\"", "dsex==\"Female\"", percentile = c(0, 50, 98), returnSimpleDoF = TRUE)
  g2pq$call <- g2p$call # the call is different, so fix that
  expect_known_value(g2pq, "gap_main_percentile.rds", update = FALSE)
  # gap achievement levels, discrete
  expect_known_value(g1al <- gap("composite", sdf, dsex == "Male", dsex == "Female", achievementLevel = "Prof", achievementDiscrete = TRUE), "gap_AL1.rds", update = FALSE)

  # check use of achievementLevel in results, that results agree across discrete and cumulative
  ga1 <- gap("composite", sdf, dsex == "Male", dsex == "Female", achievementLevel = "Advanced")
  ga2 <- gap("composite", sdf, dsex == "Male", dsex == "Female", achievementLevel = "Adv", achievementDiscrete = TRUE)
  ga1$call <- NULL
  ga2$call <- NULL
  expect_equal(ga1, ga2)
  expect_equal(ga1$results$achievementLevel, "At Advanced")
  expect_equal(ga2$results$achievementLevel, "At Advanced")

  gp1 <- gap("composite", sdf, dsex == "Male", dsex == "Female", achievementLevel = "Proficient")
  gp2 <- gap("composite", sdf, dsex == "Male", dsex == "Female", achievementLevel = "Prof", achievementDiscrete = TRUE)

  # cumulative should equal sum of discrete
  expect_equal(gp2$results$estimateA + ga1$results$estimateA, gp1$results$estimateA)
  expect_equal(gp2$results$estimateB + ga1$results$estimateB, gp1$results$estimateB)
  expect_equal(gp1$results$achievementLevel, "At or Above Proficient")
  expect_equal(gp2$results$achievementLevel, "At Proficient")

  # and cumulative, with multiple levels
  expect_known_value(g2al <- gap("composite", sdf, dsex == "Male", dsex == "Female", achievementLevel = c("Proficient", "Basic")), "gap_AL2.rds", update = FALSE)
  # gap percentage with recode
  expect_known_value(g1eq <- gap("b017451", sdf, dsex == "Male", dsex == "Female", targetLevel = "Never or hardly ever", returnSimpleDoF = TRUE), "gap_percentage1.rds", update = FALSE)
  expect_known_value(g2eq <- gap("b017451", sdf, dsex == "Male", dsex == "Female",
    targetLevel = "Infrequently",
    recode = list(b017451 = list(
      from = c(
        "Never or hardly ever",
        "Once every few weeks",
        "About once a week"
      ),
      to = c("Infrequently")
    )),
    returnSimpleDoF = TRUE
  ), "gap_percentage2.rds", update = FALSE)
  # Taylor warning
  expect_warning(gap("composite", sdf, dsex == "Male", dsex == "Female", varMethod = "Taylor"), "deprecated")
})


context("achievementLevel basic")
test_that("achievementLevel basic", {
  expect_known_value(test1 <- achievementLevels(returnCumulative = TRUE, data = sdf), file = "aLevels_test1.rds", update = FALSE)
})

context("achievementLevel, aggregated")
test_that("achievementLevel, aggregated", {
  skip_on_cran()
  expect_known_value(test2 <- achievementLevels(aggregateBy = "dsex", returnCumulative = TRUE, data = sdf), file = "aLevels_test2.rds", update = FALSE)
  # test dynamic vars
  expect_known_value(test2 <- achievementLevels(aggregateBy = dsex, returnCumulative = TRUE, data = sdf), file = "aLevels_test2.rds", update = FALSE)
  dsexVar <- "dsex"
  expect_known_value(test2 <- achievementLevels(aggregateBy = dsexVar, returnCumulative = TRUE, data = sdf), file = "aLevels_test2.rds", update = FALSE)
  skip_on_cran()
  # return to achievementLevels
  expect_known_value(test3 <- achievementLevels(aggregateBy = c("sdracem"), returnCumulative = TRUE, data = sdf), file = "aLevels_test3.rds", update = FALSE)
  expect_known_value(test4 <- achievementLevels("sdracem", aggregateBy = c("composite"), data = sdf, returnCumulative = TRUE), file = "aLevels_test4.rds", update = FALSE)
  expect_known_value(test5 <- achievementLevels("dsex", aggregateBy = c("composite"), data = sdf, returnCumulative = TRUE), file = "aLevels_test5.rds", update = FALSE)
  # Use recode to change values for specified variables:
  expect_known_value(test6 <- achievementLevels(c("composite", "dsex", "b017451"),
    aggregateBy = "dsex", sdf,
    recode = list(
      b017451 = list(
        from = c(
          "Never or hardly ever",
          "Once every few weeks",
          "About once a week"
        ),
        to = c("Infrequently")
      ),
      b017451 = list(
        from = c("2 or 3 times a week", "Every day"),
        to = c("Frequently")
      )
    )
  ), file = "aLevels_test6.rds", update = FALSE)
})

context("achievementLevel many interactions")
test_that("achievementLevel many interactions", {
  skip_on_cran()
  expect_known_value(test7 <- achievementLevels(c("composite", "ell3", "lep", "pared", "b017451"), data = sdf, returnCumulative = TRUE), file = "aLevels_test7.rds", update = FALSE)

  # iparse tests for passing different variable names
  xCols <- c("lep", "pared", "b017451")
  test7a <- achievementLevels(c("composite", "ell3", xCols), data = sdf, returnCumulative = TRUE)
  test7b <- achievementLevels(c(composite, ell3, xCols), data = sdf, returnCumulative = TRUE)
  expect_equal(test7, test7a)
  expect_equal(test7, test7b)
  expect_equal(test7a, test7b)
})

context("achievementLevel with result of zero")
test_that("achievementLevel with result of zero", {
  sdfC <- subset(sdf, scrpsu %in% 100:200)
  expect_known_value(test9 <- achievementLevels(data = subset(sdfC, pared == 1)), file = "aLevels_test9.rds", update = FALSE)
})

# tests based on sdf
context("Test correlations on SDF")
test_that("sdf correlation", {
  suppressMessages(expect_is(expect_c1_pear <- cor.sdf("b017451", "b003501", sdf, method = "Pearson", weightVar = "origwt"), "edsurveyCor"))
  skip_on_cran()
  expect_is(c1_spear <- cor.sdf("b017451", "b003501", sdf, method = "Spearman", weightVar = "origwt"), "edsurveyCor")
  # use dynamic variables
  assign(x = "b17", value = c("b017451"), envir = globalenv())
  assign(x = "b35", value = c("b003501"), envir = globalenv())
  expect_is(c1_polyc <- cor.sdf(b17, b35, sdf, method = "Polychoric", weightVar = "origwt"), "edsurveyCor") # takes awhile
  rm("b17", envir = globalenv())
  rm("b35", envir = globalenv())
  expect_is(c1_polycB <- cor.sdf(b017451, b003501, sdf, method = "Polychoric", weightVar = "origwt"), "edsurveyCor") # takes awhile
  expect_equal(c1_polyc, c1_polycB)

  sdf_dnf <- EdSurvey:::subset(sdf, b003601 == 1, verbose = FALSE)
  suppressMessages(expect_is(c2_pear <- cor.sdf("composite", "b017451", sdf_dnf, method = "Pearson", weightVar = "origwt"), "edsurveyCor"))
  expect_is(c2_spear <- cor.sdf("composite", "b017451", sdf_dnf, method = "Spearman", weightVar = "origwt"), "edsurveyCor")
  expect_is(c2_polys <- cor.sdf("composite", "b017451", sdf_dnf, method = "Polyserial", weightVar = "origwt"), "edsurveyCor")
})

context("In cor, variables as class character return errors")
test_that("In cor, variables as class", {
  skip_on_cran()
  df <- getData(sdf, c("b017451", "sdracem", "origwt"), addAttributes = TRUE)
  df$sdracem <- as.character(df$sdracem)
  expect_error(cor.sdf("b017451", "sdracem", df, method = "Pearson"))
})

context("Reordering a variable manually vs through cor.sdf")
test_that("Reordering a variable manually vs through cor.sdf", {
  skip_on_cran()
  gddat <- getData(sdf, c("b017451", "sdracem", "origwt"), addAttributes = TRUE)
  # cast sdracem as a character and then reorder based on that
  gddat$sdracem <- lfactor(as.character(gddat$sdracem), levels = c(1, 2, 3, 4, 5, 6), labels = c("White", "Hispanic", "Black", "Asian/Pacific Island", "Amer Ind/Alaska Natv", "Other"))
  cor3 <- cor.sdf("b017451", "sdracem", sdf, method = "Pearson", weightVar = "origwt", reorder = list(sdracem = c("White", "Hispanic", "Black", "Asian/Pacific Island", "Amer Ind/Alaska Natv", "Other")))
  cor4 <- cor.sdf("b017451", "sdracem", gddat, method = "Pearson", weightVar = "origwt")
  expect_equal(cor3, cor4)

  gddat$sdracem[gddat$sdracem == "Hispanic"] <- "White"
  gddat$sdracem <- factor(gddat$sdracem, levels = c("White", "Black", "Asian/Pacific Island", "Amer Ind/Alaska Natv", "Other"))
  cor1Pe <- cor.sdf("b017451", "sdracem", sdf, method = "Pearson", weightVar = "origwt", recode = list(sdracem = list(from = "Hispanic", to = "White")))
  cor2Pe <- cor.sdf("b017451", "sdracem", gddat, method = "Pearson", weightVar = "origwt")
  expect_equal(cor1Pe, cor2Pe)
  # here lfacor not condensed
  cor3Pe <- cor.sdf("b017451", "sdracem", sdf, method = "Pearson", weightVar = "origwt", recode = list(sdracem = list(from = "Hispanic", to = "White")), condenseLevels = FALSE)
  expect_equal(cor3Pe$correlation, -0.00299202137069835)
  #
  cor4Pe <- cor.sdf("b017451", "sdracem", gddat, method = "Pearson", weightVar = "origwt", condenseLevels = FALSE)
  expect_equal(cor4Pe$correlation, -0.00232480033396954)

  cor1Sp <- cor.sdf("b017451", "sdracem", sdf, method = "Spearman", weightVar = "origwt", recode = list(sdracem = list(from = "Hispanic", to = "White")))
  cor2Sp <- cor.sdf("b017451", "sdracem", gddat, method = "Spearman", weightVar = "origwt")
  expect_equal(cor1Sp, cor2Sp)

  cor1pc <- cor.sdf("b017451", "sdracem", sdf, method = "Polychoric", weightVar = "origwt", recode = list(sdracem = list(from = "Hispanic", to = "White")))
  cor2pc <- cor.sdf("b017451", "sdracem", gddat, method = "Polychoric", weightVar = "origwt")
  expect_equal(cor1pc, cor2pc)

  sdf_dnf <- EdSurvey:::subset(sdf, sdracem == 5 | sdracem == 3 | sdracem == 1, verbose = FALSE)
  cc1_pear <- cor.sdf("b017451", "sdracem", sdf_dnf, method = "Pearson", weightVar = "origwt", recode = list(sdracem = list(from = 3, to = 1)))
  cc2_pear <- cor.sdf("b017451", "sdracem", sdf_dnf, method = "Pearson", weightVar = "origwt", recode = list(sdracem = list(from = "Hispanic", to = "White")))
  expect_equal(cc1_pear, cc2_pear)
  cc1_spear <- cor.sdf("b017451", "sdracem", sdf_dnf, method = "Spearman", weightVar = "origwt", recode = list(sdracem = list(from = 3, to = 1)))
  cc2_spear <- cor.sdf("b017451", "sdracem", sdf_dnf, method = "Spearman", weightVar = "origwt", recode = list(sdracem = list(from = "Hispanic", to = "White")))
  expect_equal(cc1_spear, cc2_spear)
  cc1_polyc <- cor.sdf("b017451", "sdracem", sdf_dnf, method = "Polychoric", weightVar = "origwt", recode = list(sdracem = list(from = 3, to = 1)))
  cc2_polyc <- cor.sdf("b017451", "sdracem", sdf_dnf, method = "Polychoric", weightVar = "origwt", recode = list(sdracem = list(from = "Hispanic", to = "White")))
  expect_equal(cc1_polyc, cc2_polyc)
})

context("cor.sdf no level condensation")
test_that("cor.sdf no level condensation", {
  skip_on_cran()
  cor_nocondense <- cor.sdf(x = "c046501", y = "c044006", data = sdf, condenseLevels = FALSE)
  withr::with_options(
    list(digits = 4),
    cor_nocondenseC <- capture.output(cor_nocondense)
  )
  expect_equal(cor_nocondenseC, cor_nocondenseREF)
})

context("unweighted cor")
test_that("unweighted cor", {
  skip_on_cran()
  b1a <- cor.sdf("m815401", "b017451", method = "Pearson", sdf, weightVar = "origwt")
  b1b <- cor.sdf("m815401", "b017451", method = "Pearson", sdf, weightVar = NULL)
  # not actually equal, just approximate
  expect_equal(b1a$correlation, b1b$correlation, tolerance = 0.02, scale = 1)

  b2a <- cor.sdf("m815401", "b017451", method = "Spearman", sdf, weightVar = "origwt")
  b2b <- cor.sdf("m815401", "b017451", method = "Spearman", sdf, weightVar = NULL)
  # not actually equal, just approximate
  expect_equal(b2a$correlation, b2b$correlation, tolerance = 0.02, scale = 1)
})


context("glm")
test_that("glm", {
  skip_on_cran()
  # data to test against
  logitDat <- getData(data = sdf, varnames = c("iep", "b017451", "dsex", "b013801", "origwt", "composite", "geometry"), dropOmittedLevels = FALSE, addAttributes = TRUE)

  # test logit with no PVs
  logit0 <- logit.sdf(I(iep %in% "Yes") ~ dsex + b013801, data = sdf)
  # run just coef
  logitDat0 <- getData(data = logitDat, varnames = c("iep", "dsex", "b013801", "origwt"), dropOmittedLevels = TRUE)
  logitDat0$iepY <- ifelse(logitDat0$iep %in% "Yes", 1, 0)
  suppressWarnings(ccoef <- coef(glm.sdf(iepY ~ dsex + b013801, data = logitDat0, weightVar = "origwt", family = binomial(link = "logit"))))
  expect_equal(coef(logit0), ccoef)

  logit1 <- logit.sdf(I(composite > 300) ~ dsex + b013801, data = sdf)
  logitDat1 <- getData(data = logitDat, varnames = c("composite", "dsex", "b013801", "origwt"), dropOmittedLevels = TRUE)
  ccoef <- sapply(getPlausibleValue("composite", sdf), function(ci) {
    logitDat1$outcome <- ifelse(logitDat1[, ci] > 300, 1, 0)
    suppressWarnings(coef(glm.sdf(outcome ~ dsex + b013801, data = logitDat1, weightVar = "origwt", family = binomial(link = "logit"))))
  })
  ccoef <- apply(ccoef, 1, mean)
  expect_equal(coef(logit1), ccoef, tolerance = 1E-6)

  # test logit with PVs, multiple tests on that PV
  logit1b <- logit.sdf(I(composite > 300 & composite < 350) ~ dsex + b013801, data = sdf)
  ccoef <- sapply(getPlausibleValue("composite", sdf), function(ci) {
    logitDat1$outcome <- ifelse(logitDat1[, ci] > 300 & logitDat1[, ci] < 350, 1, 0)
    suppressWarnings(coef(glm.sdf(outcome ~ dsex + b013801, data = logitDat1, weightVar = "origwt", family = binomial(link = "logit"))))
  })
  ccoef <- apply(ccoef, 1, mean)
  expect_equal(coef(logit1b), ccoef, tolerance = 1E-6)

  # two survey items
  logit2 <- logit.sdf(I(iep %in% "Yes" & b017451 %in% "Every day") ~ dsex + b013801, data = sdf)
  logitDat2 <- getData(data = logitDat, varnames = c("iep", "b017451", "dsex", "b013801", "origwt"), dropOmittedLevels = TRUE)
  logitDat2$outcome <- ifelse(logitDat2$iep %in% "Yes" & logitDat2$b017451 %in% "Every day", 1, 0)
  suppressWarnings(ccoef <- coef(glm.sdf(outcome ~ dsex + b013801, data = logitDat2, weightVar = "origwt", family = binomial(link = "logit"))))
  expect_equal(coef(logit2), ccoef, tolerance = 1E-6)

  # a PV with a relation and a survey item
  logit3 <- logit.sdf(I(composite > 300 & b017451 %in% "Every day") ~ dsex + b013801, data = sdf)
  logitDat3 <- getData(data = logitDat, varnames = c("composite", "dsex", "b017451", "b013801", "origwt"), dropOmittedLevels = TRUE)
  ccoef <- sapply(getPlausibleValue("composite", sdf), function(ci) {
    logitDat3$outcome <- ifelse(logitDat3[, ci] > 300 & logitDat3$b017451 %in% "Every day", 1, 0)
    suppressWarnings(coef(glm.sdf(outcome ~ dsex + b013801, data = logitDat3, weightVar = "origwt", family = binomial(link = "logit"))))
  })
  ccoef <- apply(ccoef, 1, mean)
  expect_equal(coef(logit3), ccoef, tolerance = 1E-6)

  # a relation between two PVs
  logit4 <- logit.sdf(I(composite > geometry) ~ dsex + b013801, data = sdf)
  logitDat4 <- getData(data = logitDat, varnames = c("composite", "geometry", "dsex", "b013801", "origwt"), dropOmittedLevels = TRUE)
  ccoef <- sapply(1:5, function(ii) {
    ci <- getPlausibleValue("composite", sdf)[ii]
    gi <- getPlausibleValue("geometry", sdf)[ii]
    logitDat4$outcome <- ifelse(logitDat4[, ci] > logitDat4[, gi], 1, 0)
    suppressWarnings(coef(glm.sdf(outcome ~ dsex + b013801, data = logitDat4, weightVar = "origwt", family = binomial(link = "logit"))))
  })
  ccoef <- apply(ccoef, 1, mean)
  expect_equal(coef(logit4), ccoef, tolerance = 1E-6)

  # two PVs with seperate relations
  logit4b <- logit.sdf(I(composite > 300 & geometry < 350) ~ dsex + b013801, data = sdf)
  ccoef <- sapply(1:5, function(ii) {
    ci <- getPlausibleValue("composite", sdf)[ii]
    gi <- getPlausibleValue("geometry", sdf)[ii]
    logitDat4$outcome <- ifelse(logitDat4[, ci] > 300 & logitDat4[, gi] < 350, 1, 0)
    suppressWarnings(coef(glm.sdf(outcome ~ dsex + b013801, data = logitDat4, weightVar = "origwt", family = binomial(link = "logit"))))
  })
  ccoef <- apply(ccoef, 1, mean)
  expect_equal(coef(logit4b), ccoef, tolerance = 1E-6)

  skip_on_cran()
  # test logit with PVs
  withr::with_options(
    list(digits = 4),
    co <- capture.output(summary(logit1))
  )
  expect_equal(co, logit1REF)
  logit2t <- logit.sdf(I(iep %in% "Yes" & b017451 %in% "Every day") ~ dsex + b013801, data = sdf, varMethod = "Taylor")
  co <- capture.output(summary(logit2t))
  expect_equal(co, logit2tREF)
})

context("Wald test")
test_that("Wald test", {
  # glm example
  skip_on_cran()
  myLogit <- logit.sdf(dsex ~ b017451 + b003501, data = sdf, returnNumberOfPSU = TRUE)
  wt_glm <- waldTest(model = myLogit, coefficients = 2:5)
  wt1 <- capture.output(wt_glm)
  expect_equal(wt1, wt1REF)

  # lm example
  fit <- lm.sdf(composite ~ dsex + b017451, data = sdf, returnNumberOfPSU = TRUE)
  wt_lm <- waldTest(model = fit, coefficients = "b017451")
  wt2 <- capture.output(wt_lm)
  expect_equal(wt2, wt2REF)

  # test weightVar with iparse (remove call as they won't match, but numbers will)
  fita <- lm.sdf(composite ~ dsex + b017451, data = sdf, weightVar = "origwt", returnNumberOfPSU = TRUE)
  fitb <- lm.sdf(composite ~ dsex + b017451, data = sdf, weightVar = origwt, returnNumberOfPSU = TRUE)
  fit$call <- NULL
  fita$call <- NULL
  fitb$call <- NULL
  expect_equal(fit, fita) # compare to original
  expect_equal(fit, fitb)
  expect_equal(fita, fitb) # compare to each other

  # lm example with Taylor
  fit <- lm.sdf(composite ~ dsex + b017451, data = sdf, varMethod = "Taylor", returnNumberOfPSU = TRUE)
  wt_lm <- waldTest(model = fit, coefficients = "b017451")
  wt3 <- capture.output(wt_lm)
  expect_equal(wt3, wt3REF)

  # lesdf example should return warning for waldTest because strata and PSU vars needed
  # used to be an error, but chi-sq is still possible, so now warning
  gddat <- getData(
    data = sdf,
    varnames = c("composite", "dsex", "b017451", "b003501", "origwt"),
    addAttributes = TRUE
  )
  myLogit2 <- logit.sdf(dsex ~ b017451 + b003501, data = gddat, returnVarEstInputs = TRUE)
  expect_warning(waldTest(myLogit2, coefficients = 2:5))

  # lesdf this example should work, including the PSU and stratum vars
  gddat <- getData(
    data = sdf,
    varnames = c("composite", "dsex", "b017451", "b003501", "origwt", getPSUVar(sdf), getStratumVar(sdf)),
    addAttributes = TRUE
  )
  myLogit <- logit.sdf(dsex ~ b017451 + b003501, data = gddat, returnVarEstInputs = TRUE)
  wt_lesdf <- waldTest(myLogit, coefficients = 2:5)
  wt4 <- capture.output(wt_lesdf)
  expect_equal(wt4, wt4REF)
})

context("edsurvey with $ method")
test_that("edsurvey with $ method", {
  # $ method work for existing attributes
  expect_equal(sdf$country, "USA")
  # $ method return a vector
  expect_equal(class(sdf$dsex), c("lfactor", "factor"))
  # that vector has data
  dsexTab <- structure(c(Male = 8486L, Female = 8429L),
    .Dim = 2L,
    .Dimnames = structure(list(c("Male", "Female")), .Names = ""),
    class = "table"
  )
  expect_equal(table(sdf$dsex), dsexTab)

  # $ method return a data.frame
  expect_equal(class(sdf$composite), "data.frame")
})


context("levelsSDF n")
test_that("levelsSDF n", {
  skip_on_cran()
  levelRes <- levelsSDF(varnames = "pared", data = sdf)
  sum2Res <- summary2(sdf, "pared")
  mergeRes <- merge(sum2Res$summary, levelRes$pared, by.x = "pared", by.y = "labels")
  expect_equal(mergeRes$N, mergeRes$n)
  sum2ResB <- summary2(sdf, c("dsex", "pared"))
  withr::with_options(
    list(digits = 2),
    co <- capture.output(sum2ResB)
  )
  expect_equal(co, sum2ResBREF)
})


context("levelsSDF with multiple recodes")
test_that("levelsSDF with multiple recodes", {
  skip_on_cran()
  # $ method work for existing attributes
  df <- recode.sdf(sdf, recode = list(
    t088301 = list(
      from = c("Yes, available", "Yes, I have access"),
      to = c("Yes")
    ),
    t088301 = list(
      from = c("No, have no access"),
      to = c("No")
    )
  ))
  df <- recode.sdf(df, recode = list(pared = list(
    from = c("Did not finish H.S.", "Graduated H.S."),
    to = c("Graduated High School")
  )))

  levelsSDFoutput <- c(
    "Levels for Variable 't088301' (Lowest level first):",
    "    8. Omitted* (n = 84)",
    "    0. Multiple* (n = 1)",
    "    9. Yes (n = 14683)",
    "    10. No (n = 323)",
    "    NOTE: * indicates an omitted level."
  )
  colsdf <- capture.output(levelsSDF("t088301", df))
  expect_equal(levelsSDFoutput, colsdf)
})

context("use returnNumberOfPSU")
test_that("use returnNumberOfPSU", {
  skip_on_cran()
  # percentile
  pctPSU <- percentile("composite", percentiles = c(10, 50), data = sdf, returnNumberOfPSU = TRUE)
  expect_equal(attr(pctPSU, "nPSU"), 124)
  # lm.sdf
  lmPSU <- lm.sdf(composite ~ dsex, data = sdf, returnNumberOfPSU = TRUE)
  expect_equal(lmPSU$nPSU, 124)
  # gap
  gapPSU <- gap("composite", data = sdf, groupA = dsex %in% "Male", groupB = dsex %in% "Female", returnNumberOfPSU = TRUE)
  expect_equal(capture.output(gapPSU), gapPSUREF)
})


context("summary2")
test_that("summary2", {
  # Weighted with PV
  skip_on_cran()
  sPV_w <- capture.output(summary2(sdf, c("composite", "algebra")))
  expect_equal(sPV_w, sPV_wREF)

  # Unweighted
  sPV <- capture.output(summary2(sdf, "composite", weightVar = NULL))
  expect_equal(sPV, sPVREF)

  # Weighted discrete
  withr::with_options(
    list(digits = 2),
    sDiscrete_w <- capture.output(summary2(sdf, c("b017451", "dsex")))
  )
  expect_equal(sDiscrete_w, sDiscrete_wREF)

  # Unweighted discrete
  withr::with_options(
    list(digits = 4),
    sDiscrete <- capture.output(summary2(sdf, "dsex", weightVar = NULL))
  )
  expect_equal(sDiscrete, sDiscreteREF)
})

context("rq.sdf")
test_that("rq.sdf", {
  skip_on_cran()
  options(width = 500)
  rq1 <- rq.sdf(composite ~ dsex + b017451, data = sdf, tau = 0.8)
  rq1c <- withr::with_options(list(digits = 2), capture.output(summary(rq1)))
  expect_equal(rq1c, rq1REF)
})

context("mml.sdf")
test_that("mml.sdf", {
  skip_on_cran()
  # run subtest
  invisible(withr::with_options(
    list(digits = 4),
    capture.output(
      mmlNAEP <- suppressWarnings(mml.sdf(algebra ~ 1, subset(sdf, dsex == "Female"), weightVar = "origwt", verbose = TRUE))
    )
  ))
  # capture output
  # intercept
  coInt <- withr::with_options(list(digits = 4), capture.output(mmlNAEP))
  expect_equal(coInt, mmlIntREF)
  # summary
  coSum <- withr::with_options(
    list(digits = 4),
    capture.output(summary(mmlNAEP))
  )
  # don't compare the 'iterations = ##' value, remove that from the comparison
  expect_equal(dropIterations(coSum), dropIterations(mmlSumREF))

  # ensure all test names match pvvar names and vis-versa
  testNames <- c(sdf$testData$subtest, sdf$testData$test)
  pvs <- names(sdf$pvvars)
  expect_true(all(pvs %in% testNames) && all(testNames %in% pvs))
  skip_on_cran()

  # test with a fixed up pvvar name
  invisible(withr::with_options(
    list(digits = 4),
    co <- capture.output(
      suppressWarnings(
        mml.sdf(data_anal_prob ~ 1, sdf, weightVar = "origwt", verbose = TRUE)
      )
    )
  ))

  coREF <- c(
    "  (Intercept) Population SD ",
    "       279.84         40.98 "
  )
  expect_equal(co, coREF)

  # run with regressor
  invisible(withr::with_options(
    list(digits = 4),
    capture.output(
      mmlDsexNaep <- suppressWarnings(mml.sdf(algebra ~ dsex, sdf, weightVar = "origwt", verbose = TRUE))
    )
  ))
  # capture output
  # intercept
  coDsexInt <- withr::with_options(
    list(digits = 2),
    capture.output(mmlDsexNaep)
  )

  expect_equal(coDsexInt, mmlDsexIntREF)
})

context("no PSU var error and warnings")
test_that("no PSU var error and warnings", {
  skip_on_cran()
  # these warnings relatete to missing PSU so the count of PSUs will not be returned or
  # errors about how Taylor series is not possible without a PSU var
  expect_warning(
    sdfNoPSU <- edsurvey.data.frame(
      userConditions = sdf$userConditions,
      defaultConditions = sdf$defaultConditions,
      dataList = sdf$dataList,
      weights = sdf$weights,
      pvvars = sdf$pvvars,
      subject = sdf$subject,
      year = sdf$year,
      assessmentCode = sdf$assessmentCode,
      dataType = sdf$dataType,
      gradeLevel = sdf$gradeLevel,
      achievementLevels = sdf$achievementLevels,
      omittedLevels = sdf$omittedLevels,
      survey = sdf$survey,
      country = sdf$country,
      psuVar = NULL, # remove the PSU var for testing
      stratumVar = NULL, # remove the stratum var for testing
      jkSumMultiplier = sdf$jkSumMultiplier,
      recodes = sdf$recodes,
      validateFactorLabels = sdf$validateFactorLabels,
      forceLower = TRUE,
      reqDecimalConversion = sdf$reqDecimalConversion,
      fr2Path = sdf$fr2Path,
      dim0 = sdf$dim0
    ), "Taylor series"
  )
  expect_warning(rq1 <- rq.sdf(composite ~ dsex + b017451, data = sdfNoPSU, tau = 0.8, returnNumberOfPSU = TRUE), "returnNumberOfPSU")
  expect_error(res <- edsurveyTable(composite ~ dsex, data = sdfNoPSU, varMethod = "Taylor"), "jackknife")
  expect_error(res <- glm.sdf(composite ~ dsex, data = sdfNoPSU, varMethod = "Taylor", family = binomial()), "primary sampling unit")
  expect_warning(res <- glm.sdf(composite ~ dsex, data = sdfNoPSU, family = binomial(), returnNumberOfPSU = TRUE), "FALSE")
  expect_warning(res <- lm.sdf(composite ~ dsex, data = sdfNoPSU, returnNumberOfPSU = TRUE), "FALSE")
  expect_error(res <- lm.sdf(composite ~ dsex, data = sdfNoPSU, varMethod = "Taylor"), "jackknife")
  expect_error(res <- lm.sdf(composite ~ dsex, data = sdfNoPSU, varMethod = "T", returnNumberOfPSU = TRUE), "jackknife")
})